gs_power_ahr: Group sequential design power using average hazard ratio...
In keaven/gsdmvn: Group sequential design with non-constant effect

Description Usage Arguments Details Value Examples

Group sequential design power using average hazard ratio under non-proportional hazards

gs_power_ahr(
  enrollRates = tibble::tibble(Stratum = "All", duration = c(2, 2, 10), rate = c(3, 6,
    9)),
  failRates = tibble::tibble(Stratum = "All", duration = c(3, 100), failRate =
    log(2)/c(9, 18), hr = c(0.9, 0.6), dropoutRate = rep(0.001, 2)),
  ratio = 1,
  events = c(30, 40, 50),
  analysisTimes = NULL,
  binding = FALSE,
  upper = gs_b,
  upar = gsDesign(k = length(events), test.type = 1, n.I = events, maxn.IPlan =
    max(events), sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(0.1), rep(-Inf, length(events) - 1)),
  test_upper = TRUE,
  test_lower = TRUE,
  r = 18,
  tol = 1e-06
)

`enrollRates`	enrollment rates
`failRates`	failure and dropout rates
`ratio`	Experimental:Control randomization ratio (not yet implemented)
`events`	Targeted events at each analysis
`analysisTimes`	Minimum time of analysis
`binding`	indicator of whether futility bound is binding; default of FALSE is recommended
`upper`	Function to compute upper bound
`upar`	Parameter passed to `upper()`
`lower`	Function to compute lower bound
`lpar`	Parameter passed to `lower()`
`test_upper`	indicator of which analyses should include an upper (efficacy) bound; single value of TRUE (default) indicates all analyses; otherwise, a logical vector of the same length as `info` should indicate which analyses will have an efficacy bound
`test_lower`	indicator of which analyses should include an lower bound; single value of TRUE (default) indicates all analyses; single value FALSE indicated no lower bound; otherwise, a logical vector of the same length as `info` should indicate which analyses will have a lower bound
`r`	Integer, at least 2; default of 18 recommended by Jennison and Turnbull
`tol`	Tolerance parameter for boundary convergence (on Z-scale)

Bound satisfy input upper bound specification in upper, upar and lower bound specification in lower, lpar. The AHR() function computes statistical information at targeted event times. The tEvents() function is used to get events and average HR at targeted analysisTimes.

a tibble with columns Analysis, Bound, Z, Probability, theta, Time, AHR, Events. Contains a row for each analysis and each bound.

library(gsDesign2)
gs_power_ahr() %>% filter(abs(Z) < Inf)

# 2-sided symmetric O'Brien-Fleming spending bound
gs_power_ahr(analysisTimes = c(12, 24, 36),
              binding = TRUE,
              upper = gs_spending_bound,
              upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL, theta=0),
              lower = gs_spending_bound,
              lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL, theta=0))